Potential of genetic algorithms in protein folding and protein engineering simulations

Genetic algorithms are very efficient search mechanisms whichmutate, recombine and select amongst tentative solutions toa problem until a near optimal one is achieved. We introducethem as a new tool to study proteins. The identification andmotivation for different fitness functions is discussed. Theevolution of the zinc finger sequence motif from a random startis modelled. User specified changes of the repressor structurewere simulated and critical sites and exchanges for mutagenesisidentified. Vast conformational spaces are efficiently searchedas illustrated by the ab initio folding of a model protein ofa four ß strand bundle. The genetic algorithm simulationwhich mimicked important folding constraints as overall hydrophobicpackaging and a propensity of the betaphilic residues for transpositions achieved a unique fold. Cooperativity in the ßstrand regions and a length of 3–5 for the interconnectingloops was critical. Specific interaction sites were considerablyless effective in driving the fold.